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Bio Table Matchmaker

Merge two biological tables with automatic ID normalization, pre-join match diagnostics, five join types, and reproducible pandas/dplyr scripts. Built-in AI agent assistant support.

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Key facts

Key facts about Bio Table Matchmaker
FactValue
InputTwo CSV/TSV tables; max 5,000 rows each; comma/tab/semicolon auto-detect
ID normalizationEnsembl version strip, chr prefix harmonize, optional case-insensitive
DiagnosticsMatch %, unmatched A, orphans B, duplicate keys in B
Join typesLeft, inner, full outer, anti, semi
ExportCSV, TSV, clipboard; Python/R code snippets
Runs in browserYes — client-side; optional API/MCP
AI assistantBuilt-in; configures joins via manifest

What it does

Joining differential-expression results to annotation files fails silently when Ensembl IDs carry version suffixes, chromosome names disagree (chr1 vs 1), or key columns use different ID namespaces. Bio Table Matchmaker is a browser workspace for merging two CSV or TSV tables (up to 5,000 rows each) with biology-aware normalization before any rows are combined.

On the Matchmaker tab, paste or upload Table A and Table B, toggle header rows, and pick join keys. The tool auto-detects ID types (Ensembl, Entrez, gene symbol, chromosome) and shows match rate, unmatched keys, orphan rows in B, and duplicate-key warnings before you run the join. Normalization options strip Ensembl version suffixes, harmonize chr prefixes, and optionally match case-insensitively. Choose left, inner, full outer, anti-join, or semi-join; select which Table B columns to append; preview the first 50 merged rows; then download CSV/TSV or copy to clipboard. The Code tab exports matching Python pandas, R dplyr, or base merge snippets.

Why researchers use it

  • Catch ID mismatches before a join corrupts downstream enrichment or plotting
  • Strip Ensembl .version suffixes automatically so ENSG00000141510.15 matches ENSG00000141510
  • Harmonize chr1 vs 1 naming without manually editing every row
  • Inspect unmatched and orphan keys inline instead of discovering gaps after export
  • Run anti-join or semi-join to audit coverage between two gene lists
  • Copy reproducible pandas or dplyr code that mirrors the GUI settings

Best for

  • Left-joining DEG tables to GO or pathway annotation files on Ensembl or symbol
  • Merging count matrices with sample metadata keyed on gene ID
  • Finding genes present in a results file but missing from a reference annotation (anti-join)
  • Validating match rate between two exports from different pipelines before R merge
  • Quick two-table merges without opening RStudio or writing pandas boilerplate

When to use this vs alternatives

Use Bio Table Matchmaker when you need a fast, diagnostic-first merge of exactly two tables with gene ID normalization. For single-table filter, melt, pivot, and HGNC symbol cleanup, use Safe Table Bio. For comparing gene lists with Venn or UpSet diagrams, use Smart List Overlap. For genomic interval overlaps (BED), export the generated script and use bedtools locally.

What makes it different

Excel VLOOKUP treats gene IDs as plain text and breaks on versioned Ensembl accessions. Generic CSV join tools lack pre-join diagnostics and biology-specific normalization. R dplyr and Python pandas are reproducible but require scripting and offer no visual match-quality preview before execution.

Bio Table Matchmaker focuses solely on two-table key joins with domain-aware normalization (Ensembl versions, chr naming), mandatory pre-join diagnostics, five join types including anti/semi, and one-click script export — all client-side with no BioMart dependency.

How to get started

  1. Open Matchmaker and click Load Example to see a DEG + annotation join.
  2. Upload or paste Table A and Table B; confirm header toggles and key columns.
  3. Review pre-join diagnostics — click unmatched counts to preview missing keys.
  4. Adjust normalization toggles if ID types differ (Ensembl versions, chr prefix).
  5. Select join type and Table B output columns, then click Run Join.
  6. Download CSV/TSV or open Code tab to copy pandas/dplyr snippet.

Frequently asked questions

Why is my match rate 0%?
Check that key columns use the same ID namespace (Ensembl vs symbol vs Entrez). Enable Ensembl version stripping if one table has .15 suffixes and the other does not. Toggle case-insensitive matching for symbol columns with inconsistent capitalization.
What is the difference between anti-join and semi-join?
Anti-join returns rows in Table A with no match in Table B — useful for finding missing annotations. Semi-join returns rows in Table A that do match Table B, keeping only Table A columns — useful for filtering A to the B universe without appending B data.
Does the tool call BioMart or external APIs?
No. All parsing, normalization, and joining run in your browser. No data is uploaded to a server.
What happens with duplicate keys in Table B?
Left and inner joins expand to one row per matching B row (many-to-many). Diagnostics show duplicate-key counts and warn when output may exceed 10× Table A row count.
Can I use an AI agent or MCP?
Yes. The workspace assistant can configure joins from natural language. External agents call the same handler via [API & MCP](/tools/developers) using pepkio_bio-table-matchmaker.

Client source code & registry

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